Inter-frame passive-blind forgery detection for video shot based on similarity analysis

Frame insertion, deletion and duplication are common inter-frame tampering operations in digital videos. In this paper, based on similarity analysis, a passive-blind forensics scheme for video shots is proposed to detect inter-frame forgeries. This method is composed of two parts: HSV (Hue-Saturation-Value) color histogram comparison and SURF (Speeded Up Robust Features) feature extraction together with FLANN (Fast Library for Approximate Nearest Neighbors) matching for double-checking. We mainly calculate H-S and S-V color histograms of every frame in a video shot and compare the similarity between histograms to detect and locate tampered frames in the shot. Then we utilize SURF feature extraction and FLANN matching to further confirm the forgery types in the tampered locations. Experimental results demonstrate that the proposed detection method is efficient and accurate in terms of forgery identification and localization. In contrast to other inter-frame forgery detection methods, our scheme can detect three kinds of forgery operations and has its own superiority and applicability as a passive-blind detection method.